Hay-Fever 发表于 2025-3-23 13:36:20
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Joint Locality Preservation and Adaptive Combination for Graph Collaborative Filtering-items as a bipartite graph, has become the upstart in recommender systems. Nevertheless, existing GCNs based recommendation model only compromisingly exploits the shallow relationship (generally less than 4 layers) to represent the user and item with different number of interactions, which limits timmunity 发表于 2025-3-23 18:02:30
Gated Hypergraph Neural Network for Scene-Aware Recommendationisting methods only explore one or some certain components of the entire interactions. In fact, the entire interaction process is much richer and more complex, including but not limited to “who purchases what items in which merchant under what interaction environments”. Furthermore, many interaction南极 发表于 2025-3-24 01:56:31
Hyperbolic Personalized Tag Recommendationg to users’ tagging preferences. The main challenge of PTR is to learn representations of involved entities (i.e., users, items, and tags) from interaction data without loss of structural properties in original data. To this end, various PTR models have been developed to conduct representation learnDaily-Value 发表于 2025-3-24 05:42:51
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Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday KiCOMMA 发表于 2025-3-25 01:06:54
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